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Post by svart on Apr 15, 2020 13:14:44 GMT -6
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Post by svart on Apr 15, 2020 13:15:36 GMT -6
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Post by Deleted on Apr 15, 2020 20:32:28 GMT -6
Hi guys, there is a lot of misconception on what is going on apparently. This is totally understandable, because there are a lot of theories and models spreading, they are interpreted by people with whatever conclusion in mind, there is a debate on how to look at the statistics, and this is obviously a minefield. No field of actually "easier" math is so blatantly misunderstood like statistics and how curves are/can be extrapolated. No matter, what you think is the right interpretation of infection rates, death rates etc., the only safer thing to look at are the numbers that you actually want to know. How many people die and how may it look like for the nearest future. The numbers of actual death cases are, what can be considered much more reliable than the ones of infections or the mathematically drawn rates from those two actually measured numbers. The number of infections can differ a lot depending on where is tested, what is tested, how reliable are the testing methods. (Actually, the last one also applies to the death cases, unfortunately.) The easiest way to make up your own mind is to look at those numbers yourself. The curves that follow are for cumulative cases with time aligned to the outbreak time in the country and unaligned, i.e. at the same calendar dates. I suggest to look at the most reliable number, i.e. the number of deaths, to make up your own mind. All interpretation about what actually causes or would lower these can be flexed into obnoxious levels of contradiction, depending on who is doing it and who you believe knows more about it. The death cases are relatively hard numbers compared to all others. I suggest to change the axes on the graphs to "death" or "death/100k population" and leave them to linear, because this is what everyone understand intuitively in contrary to exponential/logarithmical views. These numbers are, what actually happens. And since it has been asked if the curves actually are exponential or not: If I draw an exponential curve by hand, which I am not very good at doing steadily, it could pretty much look like the US curves. On the beginning it looks a bit like a flat linear, then it becomes steeper. It starts slowly, but then... Real worlsd data is tried to be interpreted with the best mathematical function curve fitting. Since we know, that infections behave like a population growth, which is by nature exponential, it is tried to find the most accurate function coming nearest to the real world data. There are different approaches to do this, that try to minimize the error of the single point of data to the function curve. In the beginning and in the end, the exponential function can be mistaken for beeing linear or roughly simplified for the portions before and after the "knee". By no way the linearizationof the last portion of the graph is a sign to relax. A steady steep linear curve means just the same number of deaths every day. If I am informed right, NYC reached this with around 800 death cases each day. Scary, but still better than exponential. By no means I imply any political meaning. IMHO, all curves look scary still and all improvements that might be seen in the curves by bpwing down are very fragile improvements that can easily change to the worse if curcumstances change, and in the moment I am not happy at all about plans to open up schools next month gradually in my country... Of course, it is no secret, that I am more on the pessimistic side ... or as psychology says: The pessimist might be right, but the optimist definitely has more fun in life. Actually, I would love to be positively surprised in this matter here. Sometimes pictures just say more than thousand words ... Stay safe and healthy everyone. coronavirus.jhu.edu/data/cumulative-cases |
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Post by matt@IAA on Apr 15, 2020 20:39:48 GMT -6
This is like saying if we stop people from driving the way we shut down the city to lower absolute mortality due to the virus, driving wouldn’t be very risky. He needs to compare unmitigated mortality to accepted risk levels, not mitigated. |
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Post by matt@IAA on Apr 15, 2020 20:45:26 GMT -6
Man, I don’t understand that headline. Seems to be literally the opposite of what the abstract says. Here is the abstract: Background: Social distancing measures to address the U.S. COVID-19 epidemic may have significant health, social, and economic impacts. Objective: To estimate the mean change in state-level COVID-19 epidemic growth before versus after the implementation of statewide social distancing measures. Design: Interrupted time-series analysis. Setting: United States. Measurements: Our primary exposure was time in relation to implementation of the first statewide social distancing measure. The pre-implementation period began 14 days prior to implementation and included up to 3 days after implementation to account for incubation. Post-implementation began 4 days after, up to and including March 30. Our primary outcome was the COVID-19 growth rate, calculated as the log of daily COVID-19 cases minus the log of daily COVID-19 cases on the prior day. Results: All states applied some form of statewide social distancing between March 10-27. The mean daily COVID-19 growth rate decreased beginning four days after implementation of the first statewide social distancing measures, by an additional 0.8% per day; 95% CI, -1.4% to -0.2%; P=0.002). This reduction corresponds to an increase in doubling time of the epidemic from 3.3 days (before) to 5.0 days (at 14 days after implementation). Limitations: Potential bias due to the aggregate nature of the ecological data, potential confounding by contemporaneous changes (e.g., increases in testing), and potential underestimation of social distancing due to spillovers across neighboring states. Conclusion: Statewide social distancing measures were associated with a decrease in U.S. COVID-19 epidemic growth. Based on the size of the epidemic at the time of implementation in each state, social distancing measures were associated with a decrease of 3,090 cases at 7 days, and 68,255 cases at 14 days, after implementation. Edit - didn't read far enough in your actual article. Specifically linking to lockdowns, yeah, that's much more difficult to parse. I would say less that it's proof that lockdowns don't work and more that the signal to noise ratio makes it hard to see if lockdowns work. To accurately measure the steady state effect of a complex system - like a big reactor in a chemical plant, for example - you need three reaction periods for settle-out. In this case we simply didn't get them, for any of them - that would be between 12 and 42 days. Given the moral hazard, political risk, unpopularity, etc I say keep social distancing measures in the policy quiver... ditch the lockdown. |
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Post by Ward on Apr 16, 2020 10:55:54 GMT -6
Is she on to something?
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Post by svart on Apr 16, 2020 11:51:09 GMT -6
Perhaps. I think any drug is worth looking into. A common misconception is that some drugs don't work because their approved method of action is not related to this disease, such as when folks say "antibiotics don't work on viruses". While that's absolutely true, a lot of antibiotics have other effects on the body, such as anti-inflammatory effects, which are well known but not certified for use.. AKA off-label usage. I also see a lot of people actively promoting NOT investigating other chemicals and drugs for political and egotistical reasons. The COVID-19 condition is a large amount of microscopic blood clots in the lungs. It's not classical pneumonia nor is it true edema. Some doctors are having success using clot busting drugs and blood thinners. The problem is that nobody knows why this condition happens to some people, or what causes this to manifest this way. |
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Post by Johnkenn on Apr 16, 2020 11:53:53 GMT -6
Hi guys, there is a lot of misconception on what is going on apparently. This is totally understandable, because there are a lot of theories and models spreading, they are interpreted by people with whatever conclusion in mind, there is a debate on how to look at the statistics, and this is obviously a minefield. No field of actually "easier" math is so blatantly misunderstood like statistics and how curves are/can be extrapolated. No matter, what you think is the right interpretation of infection rates, death rates etc., the only safer thing to look at are the numbers that you actually want to know. How many people die and how may it look like for the nearest future. The numbers of actual death cases are, what can be considered much more reliable than the ones of infections or the mathematically drawn rates from those two actually measured numbers. The number of infections can differ a lot depending on where is tested, what is tested, how reliable are the testing methods. (Actually, the last one also applies to the death cases, unfortunately.) The easiest way to make up your own mind is to look at those numbers yourself. The curves that follow are for cumulative cases with time aligned to the outbreak time in the country and unaligned, i.e. at the same calendar dates. I suggest to look at the most reliable number, i.e. the number of deaths, to make up your own mind. All interpretation about what actually causes or would lower these can be flexed into obnoxious levels of contradiction, depending on who is doing it and who you believe knows more about it. The death cases are relatively hard numbers compared to all others. I suggest to change the axes on the graphs to "death" or "death/100k population" and leave them to linear, because this is what everyone understand intuitively in contrary to exponential/logarithmical views. These numbers are, what actually happens. And since it has been asked if the curves actually are exponential or not: If I draw an exponential curve by hand, which I am not very good at doing steadily, it could pretty much look like the US curves. On the beginning it looks a bit like a flat linear, then it becomes steeper. It starts slowly, but then... Real worlsd data is tried to be interpreted with the best mathematical function curve fitting. Since we know, that infections behave like a population growth, which is by nature exponential, it is tried to find the most accurate function coming nearest to the real world data. There are different approaches to do this, that try to minimize the error of the single point of data to the function curve. In the beginning and in the end, the exponential function can be mistaken for beeing linear or roughly simplified for the portions before and after the "knee". By no way the linearizationof the last portion of the graph is a sign to relax. A steady steep linear curve means just the same number of deaths every day. If I am informed right, NYC reached this with around 800 death cases each day. Scary, but still better than exponential. By no means I imply any political meaning. IMHO, all curves look scary still and all improvements that might be seen in the curves by bpwing down are very fragile improvements that can easily change to the worse if curcumstances change, and in the moment I am not happy at all about plans to open up schools next month gradually in my country... Of course, it is no secret, that I am more on the pessimistic side ... or as psychology says: The pessimist might be right, but the optimist definitely has more fun in life. Actually, I would love to be positively surprised in this matter here. Sometimes pictures just say more than thousand words ... Stay safe and healthy everyone. coronavirus.jhu.edu/data/cumulative-casesThat’s a very simplistic way to look at this. Antibody testing is absolutely necessary to have a rational view of all of this. |
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Post by Tbone81 on Apr 16, 2020 12:45:14 GMT -6
Working in the medical field, and knowing a lot about mechanical ventilation and pulmonary mechanics, all i have to say is people need to be careful with videos like this. She may be on to something with this medication, but videos like this are always filled with misconceptions and clinical misunderstandings. It's complicated. Acute care medicine ain't easy. Or simple.
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Post by Deleted on Apr 16, 2020 13:08:33 GMT -6
Hi guys, there is a lot of misconception on what is going on apparently. This is totally understandable, because there are a lot of theories and models spreading, they are interpreted by people with whatever conclusion in mind, there is a debate on how to look at the statistics, and this is obviously a minefield. No field of actually "easier" math is so blatantly misunderstood like statistics and how curves are/can be extrapolated. No matter, what you think is the right interpretation of infection rates, death rates etc., the only safer thing to look at are the numbers that you actually want to know. How many people die and how may it look like for the nearest future. The numbers of actual death cases are, what can be considered much more reliable than the ones of infections or the mathematically drawn rates from those two actually measured numbers. The number of infections can differ a lot depending on where is tested, what is tested, how reliable are the testing methods. (Actually, the last one also applies to the death cases, unfortunately.) The easiest way to make up your own mind is to look at those numbers yourself. The curves that follow are for cumulative cases with time aligned to the outbreak time in the country and unaligned, i.e. at the same calendar dates. I suggest to look at the most reliable number, i.e. the number of deaths, to make up your own mind. All interpretation about what actually causes or would lower these can be flexed into obnoxious levels of contradiction, depending on who is doing it and who you believe knows more about it. The death cases are relatively hard numbers compared to all others. I suggest to change the axes on the graphs to "death" or "death/100k population" and leave them to linear, because this is what everyone understand intuitively in contrary to exponential/logarithmical views. These numbers are, what actually happens. And since it has been asked if the curves actually are exponential or not: If I draw an exponential curve by hand, which I am not very good at doing steadily, it could pretty much look like the US curves. On the beginning it looks a bit like a flat linear, then it becomes steeper. It starts slowly, but then... Real worlsd data is tried to be interpreted with the best mathematical function curve fitting. Since we know, that infections behave like a population growth, which is by nature exponential, it is tried to find the most accurate function coming nearest to the real world data. There are different approaches to do this, that try to minimize the error of the single point of data to the function curve. In the beginning and in the end, the exponential function can be mistaken for beeing linear or roughly simplified for the portions before and after the "knee". By no way the linearizationof the last portion of the graph is a sign to relax. A steady steep linear curve means just the same number of deaths every day. If I am informed right, NYC reached this with around 800 death cases each day. Scary, but still better than exponential. By no means I imply any political meaning. IMHO, all curves look scary still and all improvements that might be seen in the curves by bpwing down are very fragile improvements that can easily change to the worse if curcumstances change, and in the moment I am not happy at all about plans to open up schools next month gradually in my country... Of course, it is no secret, that I am more on the pessimistic side ... or as psychology says: The pessimist might be right, but the optimist definitely has more fun in life. Actually, I would love to be positively surprised in this matter here. Sometimes pictures just say more than thousand words ... Stay safe and healthy everyone. coronavirus.jhu.edu/data/cumulative-casesThat’s a very simplistic way to look at this. Antibody testing is absolutely necessary to have a rational view of all of this. Of course it is a simplistic way. The testing method is crucial to all numbers, deaths and infections. Without any testing there is no numbers at all. There are still a lot of open questions. How reliable are the tests, how many false positives, how many false negatives, what do antibodies actually mean for covid-19, there are virus deseases where the antibodies say nothing about immunity at all. In the end, everybody works with what he has in terms of data. For example, in this thread there has been mentioned a german study about the percentage of infected people in a small town. The conclusion of the study was, that for this city the percentage was 15% *and* that this number is absolutely not scalable to any other town, federal state or country due to the amount of different factors that influence the number of infections. There are a lot of studies that conclude only slight hints on what we have with Covid-19 and people use these all the time for wrong conclusions on their own town, country, situation. The death cases are the most reliable data at all, and they still rely on factors like how a country tests their dead. A simplistic view can be a reliable abstraction if you ask the simple questions that people actually have. Is it dangerous to me and how many people will be or are actually dying. We are not talking very exact science when it comes to these statistics of cases, because the amount of (unknown) factors is huge, drawing conclusions for complexer questions from these numbers is a picky thing. If it wasn't, there would not be so many different interpretations that actually might lead to more misinformation than information. This was my point. People easily lose the focus on the fundamental data and it's meaning. |
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Post by drbill on Apr 16, 2020 13:33:41 GMT -6
Hi guys, there is a lot of misconception on what is going on apparently. Agreed. And if I used your method of focusing only (mainly?) on deaths, I would conclude that there is zero problem where I live. One death in over 2 months. I would expect more deaths than that due to heart attacks, car accidents, or appendicitis. So...no problem right? I think not. We must have a more open ended view of this thing. |
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Post by Deleted on Apr 16, 2020 13:45:49 GMT -6
Hi guys, there is a lot of misconception on what is going on apparently. Agreed. And if I used your method of focusing only (mainly?) on deaths, I would conclude that there is zero problem where I live. One death in over 2 months. I would expect more deaths than that due to heart attacks, car accidents, or appendicitis. So...no problem right? I think not. We must have a more open ended view of this thing. Yes, most probably the place you live in is lucky - for now. This obviously says nothing about what is going on a month in future, since you do not live on an island. The numbers I refer to are per country. And these are obviously not scalable to every place in the US. The US have a completely other topology, very huge differences in density of population and infrastructure, compared to e.g. Europe. And of course the whole story is "open end". A naive approach to statistics is not always a naive idea. YMMV. Everybody is free to misunderstand. |
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Post by Deleted on Apr 16, 2020 13:53:44 GMT -6
Btw, i live in such a lucky city myself. 1 death case, population 90,000.
200km from a german epicenter, the city of Hamburg.
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Post by drbill on Apr 16, 2020 15:10:21 GMT -6
The large-ish county I live in is bigger than many EU countries. Judging on a "country-wide" scale for the US is kind of silly.
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Post by svart on Apr 16, 2020 15:38:43 GMT -6
The large-ish county I live in is bigger than many EU countries. Judging on a "country-wide" scale for the US is kind of silly. Not only that but they're finding a huge range of comorbidies from region to region that greatly affect the deathrates. Saying that Mississippi has a worse case of SARS-COV2 because the death rate is higher is disingenuous when you consider they have far and away the highest number of pulmonary deaths on an average basis. They could easily have a lesser virulent strain but since the general population is much more unhealthy, end up with more deaths. Same for places like Italy with much higher average age in the regions that were hit hardest, or NYC with much higher population density, etc. The problem is that in the race to "understand", many are dismissing the very small details that make the difference in understanding! |
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Post by geoff738 on Apr 16, 2020 15:40:40 GMT -6
In the large city where I live, Toronto, over half the deaths have been in long term care, aka nursing, homes. We have not done well on that front, whereas places like Australia have.
Outside of that the curve may be bending somewhat, but deaths are still increasing day over day. We’re not close to being able to foresee when things might be able to be opened up.
I hope it can be before there is a vaccine.
Cheers,
Geoff
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Post by chessparov on Apr 16, 2020 18:31:16 GMT -6
Yesterday, when I checked... Our "Per capita" cases and deaths, were lower than Alaska! Orange County California here. Now if we didn't have L.A. County next to us.  Chris |
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Post by johneppstein on Apr 16, 2020 20:02:46 GMT -6
Except that it really doesn't (not with this disease, anyway). Which is why the CDC has (quietly, I guess they're embarassed) withdrawn all support for its use against Covid-19 And what "proposed methods of action" means, translated into English, is that they're really just guessing, they don't know, and there's no proof. Incidentally, IL6 IS interleukin 6. IL6 is just an abbreviation. What part of anything that I wrote makes you think that I don't know that IL6 is an abbreviation for interleukin 6? We don't know the method of action for a TON of drugs that we use regularly with success. Proposed method of action means - why it might work. I agree there's no proof, but there's anecdotal evidence, and we have a few clinical trials done now (randomized, not the initial one which was garbage). So far it looks like it helps clear up pneumonia faster on CT, but all of the finished studies have been small sample size. My guess is that it will be useful (if at all) for prophylaxis and to speed recovery of minor cases, but no effect on mortality. Anecdotally it doesn't help with severe cases. Anyway, here's a third method of action. i.imgur.com/aKYMFfJ.jpgThis curve shows how O2 binds to hemoglobin. It uses a prophyrin structure to bind to O2. Thats the Y axis. The rest of the oxygen is dissolved in the blood - thats PO2 on the x axis of the chart. There's a relationship between the two. As you increase PO2 you have diminishing returns, and once you hit PO2 above 60 mm Hg, you don't get any more oxygen binding to hemoglobin. Some diseases can cause that curve to shift left or right. CO poisoning shifts it to left. Sepsis shifts it to the right. COVID19 causes low PO2 because of damage to the lungs. It also may interfere with O2 binding to the porphyrin structure. Either way causes a curve shift left. Why, we don't know. But it causes less oxygen to get to your tissues, which leads to organ failure. Hydroxychloroquine is used for porphyria and has a known effect on O2 and hemoglobin. It, quinine, methylene blue all are known to shift the curve to the right. Anyway chloroquine inhibits Ebola virus in vitro but not for adults. Sometimes medicine is weird an not obvious. Well, if you go back to your post I was responding to you'll notice that you used both "IL6" and "interleukin6" in a list of agents in one sentance as if they were entries about different compounds. No parentheses or other punctuation to indicate that they are synonyms or that the one is an abbreviation for the other. I noticed that, looked it up to verify that they're the same thing, and posted the correction of the error. I'm perfectly willing to accept that it was a typing error - I make them all the time I I try to catch stuff like that when I proofread after posting. Sometimes I have to edit several times in an effort to catch everything andf sometimes something still sneaks through.
Anyway, enough with that.
BTW, you ARE aware that the reason that Trump is pushing it is not because he has any information about efficacy (and probably couldn't understand it if he did), but because he has substantial stock in the company and he's lining his pockets (or trying to) by pushing it. That's a conflict of interest and makes any endorsement of it by his administration extremely suspect and, yes, tainted.
And on top of all that, one thing that IS definitely known about the drug is that it's extremely dangerous, causing acute, unexpected (and fatal) heart failure not only among older people (like myself) who have a pre-existing heart condition, but among young, healthy people with no history of heart problems. Promoting it in a major pandemic like this is tantmount to playing Russian Roulette with the heads of thousands of desperate people.
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Post by johneppstein on Apr 16, 2020 20:17:25 GMT -6
The first entry in a Google search for "The College Fix" - their link to their own website:
In other words they make no seceret of being a right-wing propagana organ, and not even one operated and vetted by professionals - it's run by COLLEGE STUDENTS.
DEFINITELY not a reliable, unbiased source for news; their bias is displayed proudly in their masthead!
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Post by matt@IAA on Apr 16, 2020 21:11:36 GMT -6
Well, if you go back to your post I was responding to you'll notice that you used both "IL6" and "interleukin6" in a list of agents in one sentance as if they were entries about different compounds. No parentheses or other punctuation to indicate that they are synonyms or that the one is an abbreviation for the other. I noticed that, looked it up to verify that they're the same thing, and posted the correction of the error. I'm perfectly willing to accept that it was a typing error - I make them all the time I I try to catch stuff like that when I proofread after posting. Sometimes I have to edit several times in an effort to catch everything andf sometimes something still sneaks through. Anyway, enough with that. BTW, you ARE aware that the reason that Trump is pushing it is not because he has any information about efficacy (and probably couldn't understand it if he did), but because he has substantial stock in the company and he's lining his pockets (or trying to) by pushing it. That's a conflict of interest and makes any endorsement of it by his administration extremely suspect and, yes, tainted. And on top of all that, one thing that IS definitely known about the drug is that it's extremely dangerous, causing acute, unexpected (and fatal) heart failure not only among older people (like myself) who have a pre-existing heart condition, but among young, healthy people with no history of heart problems. Promoting it in a major pandemic like this is tantmount to playing Russian Roulette with the heads of thousands of desperate people.
It's normal to write a term, show the abbreviation, then use the abbreviation, no? Shoulda put parenthesis instead of commas. I think President Trump is "pushing it" because that's what he does - he's positive, on almost every topic, almost all the time. It's complete garbage to suggest that it's being promoted for profit motive - the drug isn't covered by a patent, it is generic. It's widely used in low doses, has a very low risk of side effect *especially* in short term use (as demonstrated by a meta analysis study n=~900k recently done - I think I linked it here). HCL research into SARS goes back to 2007, it's been shown to inhibit viral growth in monkeys. We know for a fact it is an IL6 inhibitor to boot. The testing wasn't started by President Trump. HCL is NOT extremely dangerous. What you wrote in bold is wrong borderlying on false. All drugs have side effects. HCL prolongs the QT interval - can cause heart arrhythmia. But no RA doc I've heard of does an EKG before prescribing it. It's routinely given as a prophylactic to people traveling in places at risk for malaria. Millions and millions of people have been given HCL. Azithromycin is also extremely safe by itself. However, combined it seems the risk of an adverse cardiac event goes up twofold, as they both have the potential to prolong the QT interval. Sounds bad, right? But two times what? Two times .0000001% is still nothing to worry about (I don't know what the risk is, but the point is that two times a small risk is still a small risk). The truth is this entire issue has been politicized. HCL was being tested AND USED before President Trump said anything - I saw a video about it on Medcram weeks before he mentioned it, it was being used in China and South Korea. It continues to be tested in clinical trials. It would have been used, and trialed, had he not said anything. The screeching from the left made the whole thing into a political football. The right dug in with an equally ignorant position, with many proclaiming it a cure (something Pres. Trump didn't even say...?!). Now people on the left are convinced its quackery, people on the right just know it works. It's inane for a governor of a state to get involved in what a doctor can prescribe to a patient. It's also inane and unethical for doctors to prescribe drugs to themselves and family in advance. Somehow now you can pretty much tell who someone voted for by their opinion on a drug that we don't have hardly ANY actual evidence of efficacy either way. That's just real dumb. Everyone sucks here. The drug either works or it doesn't. Anecdotal evidence from some doctor in NYC or LA doesn't mean it works. Neither does it mean it does not. This is literally why we do randomized controlled trials - so bias, motive, or whatever else can be sifted through. Remember, covid19 has a survival rate of something like 99.5%. If you see a hundred patients, and give them all sugar pills, you have a really good shot of having everyone get better. Sugar pulls cure covid19, yeah? That's why we do big trials, big studies, to really determine the efficacy. Some are underway -- none are done yet. Docs don't need Presidents to make good or bad decisions. There's a good shot a lot of the deaths in the spanish flu were due to aspirin overdosing. On the other hand, HCL / Plaquenil is not a new drug - it's extremely common. The risk is low, the upside unknown - why not give it a shot? (PS a lot of the crap we use HCL for has an unknown mechanism of action, and yet it still works). Resist the politicization of this! It's bad! |
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Post by drbill on Apr 16, 2020 21:19:59 GMT -6
BTW, you ARE aware that the reason that Trump is pushing it is not because he has any information about efficacy (and probably couldn't understand it if he did), but because he has substantial stock in the company and he's lining his pockets (or trying to) by pushing it. That's a conflict of interest and makes any endorsement of it by his administration extremely suspect and, yes, tainted.
John - your EXTREME hatred of Trump and anything conservative or "right wing" is getting EXTREMELY wearing..... Just so you know, you're annoying the **** out of a lot of people. Maybe you don't care, but just thought I'd be honest and let you know. |
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Post by cyrano on Apr 17, 2020 6:49:25 GMT -6
Just FYI, the test with HCL has been ended, because most patients died...
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Post by schmalzy on Apr 17, 2020 9:31:35 GMT -6
Just FYI, the test with HCL has been ended, because most patients died... Do you have a link for that? |
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Post by svart on Apr 17, 2020 9:46:09 GMT -6
Looks like there's a paper coming out that is proving that there has been very little difference between countries that locked everything down and those that didn't when accounting for all factors, including testing. It's pre-peer review right now so I won't post the link since that will only get feathers ruffled since apparently science can only be done by consensus in some people's eyes.
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Post by ericn on Apr 17, 2020 10:05:45 GMT -6
Hi guys, there is a lot of misconception on what is going on apparently. This is totally understandable, because there are a lot of theories and models spreading, they are interpreted by people with whatever conclusion in mind, there is a debate on how to look at the statistics, and this is obviously a minefield. No field of actually "easier" math is so blatantly misunderstood like statistics and how curves are/can be extrapolated. No matter, what you think is the right interpretation of infection rates, death rates etc., the only safer thing to look at are the numbers that you actually want to know. How many people die and how may it look like for the nearest future. The numbers of actual death cases are, what can be considered much more reliable than the ones of infections or the mathematically drawn rates from those two actually measured numbers. The number of infections can differ a lot depending on where is tested, what is tested, how reliable are the testing methods. (Actually, the last one also applies to the death cases, unfortunately.) The easiest way to make up your own mind is to look at those numbers yourself. The curves that follow are for cumulative cases with time aligned to the outbreak time in the country and unaligned, i.e. at the same calendar dates. I suggest to look at the most reliable number, i.e. the number of deaths, to make up your own mind. All interpretation about what actually causes or would lower these can be flexed into obnoxious levels of contradiction, depending on who is doing it and who you believe knows more about it. The death cases are relatively hard numbers compared to all others. I suggest to change the axes on the graphs to "death" or "death/100k population" and leave them to linear, because this is what everyone understand intuitively in contrary to exponential/logarithmical views. These numbers are, what actually happens. And since it has been asked if the curves actually are exponential or not: If I draw an exponential curve by hand, which I am not very good at doing steadily, it could pretty much look like the US curves. On the beginning it looks a bit like a flat linear, then it becomes steeper. It starts slowly, but then... Real worlsd data is tried to be interpreted with the best mathematical function curve fitting. Since we know, that infections behave like a population growth, which is by nature exponential, it is tried to find the most accurate function coming nearest to the real world data. There are different approaches to do this, that try to minimize the error of the single point of data to the function curve. In the beginning and in the end, the exponential function can be mistaken for beeing linear or roughly simplified for the portions before and after the "knee". By no way the linearizationof the last portion of the graph is a sign to relax. A steady steep linear curve means just the same number of deaths every day. If I am informed right, NYC reached this with around 800 death cases each day. Scary, but still better than exponential. By no means I imply any political meaning. IMHO, all curves look scary still and all improvements that might be seen in the curves by bpwing down are very fragile improvements that can easily change to the worse if curcumstances change, and in the moment I am not happy at all about plans to open up schools next month gradually in my country... Of course, it is no secret, that I am more on the pessimistic side ... or as psychology says: The pessimist might be right, but the optimist definitely has more fun in life. Actually, I would love to be positively surprised in this matter here. Sometimes pictures just say more than thousand words ... Stay safe and healthy everyone. coronavirus.jhu.edu/data/cumulative-casesActually the death rate isn’t that accurate; First the death rate is based on those with confirmed test results and reports from hospitals. Second many deaths at home are being listed as cardiac arrest or even pneumonia. Now it would be quite simple if the testing supplies were available to swab the noses of all those who die during this period and freeze these samples and then afterwards when the demand for use of those nice new Abott machines that can do a 1000 a day ( made in China FYI) could run the samples of those who passed in this time period. We would then be able to have an accurate, well close to accurate death rate in an after action report. This is what happens when you spend your entire life living with lab managers! |
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